A data driven approach to understanding the organization of high-level visual cortex

Abstract The neural representation in scene-selective regions of human visual cortex, such as the PPA, has been linked to the semantic and categorical properties of the images. However, the extent to which patterns of neural response in these regions reflect more fundamental organizing principles is...

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Autores principales:	David M. Watson, Timothy J. Andrews, Tom Hartley
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/dbb9e6e78ede4a28a10b73030804327c
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id	oai:doaj.org-article:dbb9e6e78ede4a28a10b73030804327c
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spelling	oai:doaj.org-article:dbb9e6e78ede4a28a10b73030804327c2021-12-02T11:40:43ZA data driven approach to understanding the organization of high-level visual cortex10.1038/s41598-017-03974-52045-2322https://doaj.org/article/dbb9e6e78ede4a28a10b73030804327c2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03974-5https://doaj.org/toc/2045-2322Abstract The neural representation in scene-selective regions of human visual cortex, such as the PPA, has been linked to the semantic and categorical properties of the images. However, the extent to which patterns of neural response in these regions reflect more fundamental organizing principles is not yet clear. Existing studies generally employ stimulus conditions chosen by the experimenter, potentially obscuring the contribution of more basic stimulus dimensions. To address this issue, we used a data-driven approach to describe a large database of scenes (>100,000 images) in terms of their visual properties (orientation, spatial frequency, spatial location). K-means clustering was then used to select images from distinct regions of this feature space. Images in each cluster did not correspond to typical scene categories. Nevertheless, they elicited distinct patterns of neural response in the PPA. Moreover, the similarity of the neural response to different clusters in the PPA could be predicted by the similarity in their image properties. Interestingly, the neural response in the PPA was also predicted by perceptual responses to the scenes, but not by their semantic properties. These findings provide an image-based explanation for the emergence of higher-level representations in scene-selective regions of the human brain.David M. WatsonTimothy J. AndrewsTom HartleyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-14 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David M. Watson Timothy J. Andrews Tom Hartley A data driven approach to understanding the organization of high-level visual cortex
description	Abstract The neural representation in scene-selective regions of human visual cortex, such as the PPA, has been linked to the semantic and categorical properties of the images. However, the extent to which patterns of neural response in these regions reflect more fundamental organizing principles is not yet clear. Existing studies generally employ stimulus conditions chosen by the experimenter, potentially obscuring the contribution of more basic stimulus dimensions. To address this issue, we used a data-driven approach to describe a large database of scenes (>100,000 images) in terms of their visual properties (orientation, spatial frequency, spatial location). K-means clustering was then used to select images from distinct regions of this feature space. Images in each cluster did not correspond to typical scene categories. Nevertheless, they elicited distinct patterns of neural response in the PPA. Moreover, the similarity of the neural response to different clusters in the PPA could be predicted by the similarity in their image properties. Interestingly, the neural response in the PPA was also predicted by perceptual responses to the scenes, but not by their semantic properties. These findings provide an image-based explanation for the emergence of higher-level representations in scene-selective regions of the human brain.
format	article
author	David M. Watson Timothy J. Andrews Tom Hartley
author_facet	David M. Watson Timothy J. Andrews Tom Hartley
author_sort	David M. Watson
title	A data driven approach to understanding the organization of high-level visual cortex
title_short	A data driven approach to understanding the organization of high-level visual cortex
title_full	A data driven approach to understanding the organization of high-level visual cortex
title_fullStr	A data driven approach to understanding the organization of high-level visual cortex
title_full_unstemmed	A data driven approach to understanding the organization of high-level visual cortex
title_sort	data driven approach to understanding the organization of high-level visual cortex
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/dbb9e6e78ede4a28a10b73030804327c
work_keys_str_mv	AT davidmwatson adatadrivenapproachtounderstandingtheorganizationofhighlevelvisualcortex AT timothyjandrews adatadrivenapproachtounderstandingtheorganizationofhighlevelvisualcortex AT tomhartley adatadrivenapproachtounderstandingtheorganizationofhighlevelvisualcortex AT davidmwatson datadrivenapproachtounderstandingtheorganizationofhighlevelvisualcortex AT timothyjandrews datadrivenapproachtounderstandingtheorganizationofhighlevelvisualcortex AT tomhartley datadrivenapproachtounderstandingtheorganizationofhighlevelvisualcortex
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A data driven approach to understanding the organization of high-level visual cortex

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